Jul 2017 – Aug 2017
Used snippets of smartwatch accelerometer data to identify one of 20 gestures. This required generating all features from the accelerometer data. Achieved 98% recognition accuracy on the dataset using both individual and ensemble models, if permitted to train on some of a user's previous actions; and 88% accuracy for users the system has never seen before.
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